Overview

Dataset statistics

Number of variables25
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory200.0 B

Variable types

Numeric21
Categorical4

Alerts

status_payment_sept is highly correlated with status_payment_aug and 2 other fieldsHigh correlation
status_payment_aug is highly correlated with status_payment_sept and 7 other fieldsHigh correlation
status_payment_july is highly correlated with status_payment_sept and 9 other fieldsHigh correlation
status_payment_june is highly correlated with status_payment_sept and 10 other fieldsHigh correlation
status_payment_may is highly correlated with status_payment_aug and 8 other fieldsHigh correlation
status_payment_april is highly correlated with status_payment_aug and 8 other fieldsHigh correlation
bill_sept is highly correlated with status_payment_aug and 8 other fieldsHigh correlation
bill_aug is highly correlated with status_payment_aug and 10 other fieldsHigh correlation
bill_july is highly correlated with status_payment_aug and 11 other fieldsHigh correlation
bill_june is highly correlated with status_payment_july and 13 other fieldsHigh correlation
bill_may is highly correlated with status_payment_july and 13 other fieldsHigh correlation
bill_april is highly correlated with status_payment_june and 11 other fieldsHigh correlation
previous_payment_sept is highly correlated with bill_sept and 5 other fieldsHigh correlation
previous_payment_aug is highly correlated with bill_july and 5 other fieldsHigh correlation
previous_payment_july is highly correlated with bill_june and 7 other fieldsHigh correlation
previous_payment_june is highly correlated with bill_june and 6 other fieldsHigh correlation
previous_payment_may is highly correlated with bill_june and 5 other fieldsHigh correlation
previous_payment_april is highly correlated with bill_may and 4 other fieldsHigh correlation
status_payment_sept is highly correlated with status_payment_aug and 3 other fieldsHigh correlation
status_payment_aug is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_july is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_june is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_may is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_april is highly correlated with status_payment_aug and 3 other fieldsHigh correlation
bill_sept is highly correlated with bill_aug and 4 other fieldsHigh correlation
bill_aug is highly correlated with bill_sept and 4 other fieldsHigh correlation
bill_july is highly correlated with bill_sept and 4 other fieldsHigh correlation
bill_june is highly correlated with bill_sept and 4 other fieldsHigh correlation
bill_may is highly correlated with bill_sept and 4 other fieldsHigh correlation
bill_april is highly correlated with bill_sept and 4 other fieldsHigh correlation
status_payment_sept is highly correlated with status_payment_aug and 1 other fieldsHigh correlation
status_payment_aug is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_july is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_june is highly correlated with status_payment_aug and 3 other fieldsHigh correlation
status_payment_may is highly correlated with status_payment_aug and 4 other fieldsHigh correlation
status_payment_april is highly correlated with status_payment_aug and 5 other fieldsHigh correlation
bill_sept is highly correlated with bill_aug and 4 other fieldsHigh correlation
bill_aug is highly correlated with bill_sept and 5 other fieldsHigh correlation
bill_july is highly correlated with bill_sept and 5 other fieldsHigh correlation
bill_june is highly correlated with status_payment_may and 5 other fieldsHigh correlation
bill_may is highly correlated with status_payment_april and 6 other fieldsHigh correlation
bill_april is highly correlated with status_payment_april and 6 other fieldsHigh correlation
previous_payment_sept is highly correlated with bill_augHigh correlation
previous_payment_aug is highly correlated with bill_julyHigh correlation
previous_payment_june is highly correlated with bill_mayHigh correlation
previous_payment_may is highly correlated with bill_aprilHigh correlation
LIMIT_BAL is highly correlated with bill_sept and 5 other fieldsHigh correlation
MARRIAGE is highly correlated with AGEHigh correlation
AGE is highly correlated with MARRIAGEHigh correlation
status_payment_sept is highly correlated with status_payment_aug and 5 other fieldsHigh correlation
status_payment_aug is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_july is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_june is highly correlated with status_payment_sept and 4 other fieldsHigh correlation
status_payment_may is highly correlated with status_payment_sept and 5 other fieldsHigh correlation
status_payment_april is highly correlated with status_payment_sept and 5 other fieldsHigh correlation
bill_sept is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
bill_aug is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
bill_july is highly correlated with bill_sept and 6 other fieldsHigh correlation
bill_june is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
bill_may is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
bill_april is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
previous_payment_sept is highly correlated with previous_payment_aug and 2 other fieldsHigh correlation
previous_payment_aug is highly correlated with bill_july and 3 other fieldsHigh correlation
previous_payment_july is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
previous_payment_june is highly correlated with previous_payment_sept and 1 other fieldsHigh correlation
previous_payment_may is highly correlated with bill_july and 1 other fieldsHigh correlation
target is highly correlated with status_payment_septHigh correlation
previous_payment_aug is highly skewed (γ1 = 30.45381745) Skewed
ID is uniformly distributed Uniform
ID has unique values Unique
status_payment_sept has 14737 (49.1%) zeros Zeros
status_payment_aug has 15730 (52.4%) zeros Zeros
status_payment_july has 15764 (52.5%) zeros Zeros
status_payment_june has 16455 (54.9%) zeros Zeros
status_payment_may has 16947 (56.5%) zeros Zeros
status_payment_april has 16286 (54.3%) zeros Zeros
bill_sept has 2008 (6.7%) zeros Zeros
bill_aug has 2506 (8.4%) zeros Zeros
bill_july has 2870 (9.6%) zeros Zeros
bill_june has 3195 (10.7%) zeros Zeros
bill_may has 3506 (11.7%) zeros Zeros
bill_april has 4020 (13.4%) zeros Zeros
previous_payment_sept has 5249 (17.5%) zeros Zeros
previous_payment_aug has 5396 (18.0%) zeros Zeros
previous_payment_july has 5968 (19.9%) zeros Zeros
previous_payment_june has 6408 (21.4%) zeros Zeros
previous_payment_may has 6703 (22.3%) zeros Zeros
previous_payment_april has 7173 (23.9%) zeros Zeros

Reproduction

Analysis started2023-11-06 14:32:22.348989
Analysis finished2023-11-06 14:34:29.322929
Duration2 minutes and 6.97 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15000.5
Minimum1
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:29.491027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500.95
Q17500.75
median15000.5
Q322500.25
95-th percentile28500.05
Maximum30000
Range29999
Interquartile range (IQR)14999.5

Descriptive statistics

Standard deviation8660.398374
Coefficient of variation (CV)0.5773406469
Kurtosis-1.2
Mean15000.5
Median Absolute Deviation (MAD)7500
Skewness0
Sum450015000
Variance75002500
MonotonicityStrictly increasing
2023-11-06T11:34:29.740032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
199971
 
< 0.1%
200091
 
< 0.1%
200081
 
< 0.1%
200071
 
< 0.1%
200061
 
< 0.1%
200051
 
< 0.1%
200041
 
< 0.1%
200031
 
< 0.1%
200021
 
< 0.1%
Other values (29990)29990
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
300001
< 0.1%
299991
< 0.1%
299981
< 0.1%
299971
< 0.1%
299961
< 0.1%
299951
< 0.1%
299941
< 0.1%
299931
< 0.1%
299921
< 0.1%
299911
< 0.1%

LIMIT_BAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.3227
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:30.012486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568 × 1010
MonotonicityNot monotonic
2023-11-06T11:34:30.264967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500003365
 
11.2%
200001976
 
6.6%
300001610
 
5.4%
800001567
 
5.2%
2000001528
 
5.1%
1500001110
 
3.7%
1000001048
 
3.5%
180000995
 
3.3%
360000881
 
2.9%
60000825
 
2.8%
Other values (71)15095
50.3%
ValueCountFrequency (%)
10000493
 
1.6%
160002
 
< 0.1%
200001976
6.6%
300001610
5.4%
40000230
 
0.8%
500003365
11.2%
60000825
 
2.8%
70000731
 
2.4%
800001567
5.2%
90000651
 
2.2%
ValueCountFrequency (%)
10000001
 
< 0.1%
8000002
 
< 0.1%
7800002
 
< 0.1%
7600001
 
< 0.1%
7500004
< 0.1%
7400002
 
< 0.1%
7300002
 
< 0.1%
7200003
 
< 0.1%
7100006
< 0.1%
7000008
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
18112 
1
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
218112
60.4%
111888
39.6%

Length

2023-11-06T11:34:30.507349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-11-06T11:34:30.843009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
218112
60.4%
111888
39.6%

Most occurring characters

ValueCountFrequency (%)
218112
60.4%
111888
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
218112
60.4%
111888
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
218112
60.4%
111888
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218112
60.4%
111888
39.6%

EDUCATION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
14030 
1
10585 
3
4917 
4
 
468

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
214030
46.8%
110585
35.3%
34917
 
16.4%
4468
 
1.6%

Length

2023-11-06T11:34:31.001523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-11-06T11:34:31.178951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
214030
46.8%
110585
35.3%
34917
 
16.4%
4468
 
1.6%

Most occurring characters

ValueCountFrequency (%)
214030
46.8%
110585
35.3%
34917
 
16.4%
4468
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
214030
46.8%
110585
35.3%
34917
 
16.4%
4468
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
214030
46.8%
110585
35.3%
34917
 
16.4%
4468
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
214030
46.8%
110585
35.3%
34917
 
16.4%
4468
 
1.6%

MARRIAGE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
15964 
1
13659 
3
 
377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3377
 
1.3%

Length

2023-11-06T11:34:31.347543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-11-06T11:34:31.524029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3377
 
1.3%

Most occurring characters

ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3377
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3377
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3377
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3377
 
1.3%

AGE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:31.714577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217904068
Coefficient of variation (CV)0.2597653709
Kurtosis0.04430337824
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.7322458688
Sum1064565
Variance84.96975541
MonotonicityNot monotonic
2023-11-06T11:34:31.939454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291605
 
5.3%
271477
 
4.9%
281409
 
4.7%
301395
 
4.7%
261256
 
4.2%
311217
 
4.1%
251186
 
4.0%
341162
 
3.9%
321158
 
3.9%
331146
 
3.8%
Other values (46)16989
56.6%
ValueCountFrequency (%)
2167
 
0.2%
22560
 
1.9%
23931
3.1%
241127
3.8%
251186
4.0%
261256
4.2%
271477
4.9%
281409
4.7%
291605
5.3%
301395
4.7%
ValueCountFrequency (%)
791
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
734
 
< 0.1%
723
 
< 0.1%
713
 
< 0.1%
7010
< 0.1%
6915
0.1%
685
 
< 0.1%
6716
0.1%

status_payment_sept
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2023-11-06T11:34:32.882971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.123801528
Coefficient of variation (CV)-67.29350467
Kurtosis2.720715042
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.7319749269
Sum-501
Variance1.262929874
MonotonicityNot monotonic
2023-11-06T11:34:33.053881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
014737
49.1%
-15686
 
19.0%
13688
 
12.3%
-22759
 
9.2%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
819
 
0.1%
611
 
< 0.1%
ValueCountFrequency (%)
-22759
 
9.2%
-15686
 
19.0%
014737
49.1%
13688
 
12.3%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
611
 
< 0.1%
79
 
< 0.1%
ValueCountFrequency (%)
819
 
0.1%
79
 
< 0.1%
611
 
< 0.1%
526
 
0.1%
476
 
0.3%
3322
 
1.1%
22667
 
8.9%
13688
 
12.3%
014737
49.1%
-15686
 
19.0%

status_payment_aug
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1337666667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2023-11-06T11:34:33.222937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197185973
Coefficient of variation (CV)-8.949807922
Kurtosis1.57041773
Mean-0.1337666667
Median Absolute Deviation (MAD)0
Skewness0.7905650222
Sum-4013
Variance1.433254254
MonotonicityNot monotonic
2023-11-06T11:34:33.395085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
015730
52.4%
-16050
 
20.2%
23927
 
13.1%
-23782
 
12.6%
3326
 
1.1%
499
 
0.3%
128
 
0.1%
525
 
0.1%
720
 
0.1%
612
 
< 0.1%
ValueCountFrequency (%)
-23782
 
12.6%
-16050
 
20.2%
015730
52.4%
128
 
0.1%
23927
 
13.1%
3326
 
1.1%
499
 
0.3%
525
 
0.1%
612
 
< 0.1%
720
 
0.1%
ValueCountFrequency (%)
81
 
< 0.1%
720
 
0.1%
612
 
< 0.1%
525
 
0.1%
499
 
0.3%
3326
 
1.1%
23927
 
13.1%
128
 
0.1%
015730
52.4%
-16050
 
20.2%

status_payment_july
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2023-11-06T11:34:33.570242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.196867568
Coefficient of variation (CV)-7.201369245
Kurtosis2.084435875
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.8406818269
Sum-4986
Variance1.432491976
MonotonicityNot monotonic
2023-11-06T11:34:33.736060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
015764
52.5%
-15938
 
19.8%
-24085
 
13.6%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
727
 
0.1%
623
 
0.1%
521
 
0.1%
14
 
< 0.1%
ValueCountFrequency (%)
-24085
 
13.6%
-15938
 
19.8%
015764
52.5%
14
 
< 0.1%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
521
 
0.1%
623
 
0.1%
727
 
0.1%
ValueCountFrequency (%)
83
 
< 0.1%
727
 
0.1%
623
 
0.1%
521
 
0.1%
476
 
0.3%
3240
 
0.8%
23819
 
12.7%
14
 
< 0.1%
015764
52.5%
-15938
 
19.8%

status_payment_june
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2206666667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2023-11-06T11:34:33.905938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.169138622
Coefficient of variation (CV)-5.29821128
Kurtosis3.496983496
Mean-0.2206666667
Median Absolute Deviation (MAD)0
Skewness0.9996294133
Sum-6620
Variance1.366885118
MonotonicityNot monotonic
2023-11-06T11:34:34.067634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
016455
54.9%
-15687
 
19.0%
-24348
 
14.5%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
758
 
0.2%
535
 
0.1%
65
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
-24348
 
14.5%
-15687
 
19.0%
016455
54.9%
12
 
< 0.1%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
535
 
0.1%
65
 
< 0.1%
758
 
0.2%
ValueCountFrequency (%)
82
 
< 0.1%
758
 
0.2%
65
 
< 0.1%
535
 
0.1%
469
 
0.2%
3180
 
0.6%
23159
 
10.5%
12
 
< 0.1%
016455
54.9%
-15687
 
19.0%

status_payment_may
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2023-11-06T11:34:34.232987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.133187406
Coefficient of variation (CV)-4.256902352
Kurtosis3.989748144
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197025
Sum-7986
Variance1.284113697
MonotonicityNot monotonic
2023-11-06T11:34:34.391032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016947
56.5%
-15539
 
18.5%
-24546
 
15.2%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
758
 
0.2%
517
 
0.1%
64
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
-24546
 
15.2%
-15539
 
18.5%
016947
56.5%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
517
 
0.1%
64
 
< 0.1%
758
 
0.2%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
758
 
0.2%
64
 
< 0.1%
517
 
0.1%
484
 
0.3%
3178
 
0.6%
22626
 
8.8%
016947
56.5%
-15539
 
18.5%
-24546
 
15.2%

status_payment_april
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2023-11-06T11:34:34.561960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.149987626
Coefficient of variation (CV)-3.950489954
Kurtosis3.42653413
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.9480293916
Sum-8733
Variance1.322471539
MonotonicityNot monotonic
2023-11-06T11:34:34.728512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016286
54.3%
-15740
 
19.1%
-24895
 
16.3%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
-24895
 
16.3%
-15740
 
19.1%
016286
54.3%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
513
 
< 0.1%
619
 
0.1%
746
 
0.2%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
449
 
0.2%
3184
 
0.6%
22766
 
9.2%
016286
54.3%
-15740
 
19.1%
-24895
 
16.3%

bill_sept
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2023-11-06T11:34:34.944047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
MonotonicityNot monotonic
2023-11-06T11:34:35.173914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02008
 
6.7%
390244
 
0.8%
78076
 
0.3%
32672
 
0.2%
31663
 
0.2%
250059
 
0.2%
39649
 
0.2%
240039
 
0.1%
41629
 
0.1%
50025
 
0.1%
Other values (22713)27336
91.1%
ValueCountFrequency (%)
-1655801
< 0.1%
-1549731
< 0.1%
-153081
< 0.1%
-143861
< 0.1%
-115451
< 0.1%
-106821
< 0.1%
-98021
< 0.1%
-90951
< 0.1%
-81871
< 0.1%
-74381
< 0.1%
ValueCountFrequency (%)
9645111
< 0.1%
7468141
< 0.1%
6530621
< 0.1%
6304581
< 0.1%
6266481
< 0.1%
6217491
< 0.1%
6138601
< 0.1%
6107231
< 0.1%
6085941
< 0.1%
6040191
< 0.1%

bill_aug
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07517
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2023-11-06T11:34:35.415884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
MonotonicityNot monotonic
2023-11-06T11:34:35.670496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02506
 
8.4%
390231
 
0.8%
32675
 
0.2%
78075
 
0.2%
31672
 
0.2%
39651
 
0.2%
250051
 
0.2%
240042
 
0.1%
-20029
 
0.1%
41628
 
0.1%
Other values (22336)26840
89.5%
ValueCountFrequency (%)
-697771
< 0.1%
-675261
< 0.1%
-333501
< 0.1%
-300001
< 0.1%
-262141
< 0.1%
-247041
< 0.1%
-247021
< 0.1%
-229601
< 0.1%
-186181
< 0.1%
-180881
< 0.1%
ValueCountFrequency (%)
9839311
< 0.1%
7439701
< 0.1%
6715631
< 0.1%
6467701
< 0.1%
6244751
< 0.1%
6059431
< 0.1%
5977931
< 0.1%
5868251
< 0.1%
5817751
< 0.1%
5776811
< 0.1%

bill_july
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2023-11-06T11:34:35.918965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
MonotonicityNot monotonic
2023-11-06T11:34:36.191060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02870
 
9.6%
390275
 
0.9%
78074
 
0.2%
32663
 
0.2%
31662
 
0.2%
39648
 
0.2%
250040
 
0.1%
240039
 
0.1%
41629
 
0.1%
20027
 
0.1%
Other values (22016)26473
88.2%
ValueCountFrequency (%)
-1572641
< 0.1%
-615061
< 0.1%
-461271
< 0.1%
-340411
< 0.1%
-254431
< 0.1%
-247021
< 0.1%
-203201
< 0.1%
-177061
< 0.1%
-159101
< 0.1%
-156411
< 0.1%
ValueCountFrequency (%)
16640891
< 0.1%
8550861
< 0.1%
6931311
< 0.1%
6896431
< 0.1%
6896271
< 0.1%
6320411
< 0.1%
5974151
< 0.1%
5789711
< 0.1%
5779571
< 0.1%
5770151
< 0.1%

bill_june
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94897
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2023-11-06T11:34:36.482985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
MonotonicityNot monotonic
2023-11-06T11:34:36.795563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03195
 
10.7%
390246
 
0.8%
780101
 
0.3%
31668
 
0.2%
32662
 
0.2%
39644
 
0.1%
240039
 
0.1%
15039
 
0.1%
250034
 
0.1%
41633
 
0.1%
Other values (21538)26139
87.1%
ValueCountFrequency (%)
-1700001
< 0.1%
-813341
< 0.1%
-651671
< 0.1%
-506161
< 0.1%
-466271
< 0.1%
-345031
< 0.1%
-274901
< 0.1%
-243031
< 0.1%
-221081
< 0.1%
-203201
< 0.1%
ValueCountFrequency (%)
8915861
< 0.1%
7068641
< 0.1%
6286991
< 0.1%
6168361
< 0.1%
5728051
< 0.1%
5690341
< 0.1%
5656691
< 0.1%
5635431
< 0.1%
5480201
< 0.1%
5426531
< 0.1%

bill_may
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40097
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2023-11-06T11:34:37.054160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
MonotonicityNot monotonic
2023-11-06T11:34:37.319170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03506
 
11.7%
390235
 
0.8%
78094
 
0.3%
31679
 
0.3%
32662
 
0.2%
15058
 
0.2%
39647
 
0.2%
240039
 
0.1%
250037
 
0.1%
41636
 
0.1%
Other values (21000)25807
86.0%
ValueCountFrequency (%)
-813341
< 0.1%
-613721
< 0.1%
-530071
< 0.1%
-466271
< 0.1%
-375941
< 0.1%
-361561
< 0.1%
-304811
< 0.1%
-283351
< 0.1%
-230031
< 0.1%
-207531
< 0.1%
ValueCountFrequency (%)
9271711
< 0.1%
8235401
< 0.1%
5870671
< 0.1%
5517021
< 0.1%
5478801
< 0.1%
5306721
< 0.1%
5243151
< 0.1%
5161391
< 0.1%
5141141
< 0.1%
5082131
< 0.1%

bill_april
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2023-11-06T11:34:37.607013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
MonotonicityNot monotonic
2023-11-06T11:34:37.919374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04020
 
13.4%
390207
 
0.7%
78086
 
0.3%
15078
 
0.3%
31677
 
0.3%
32656
 
0.2%
39645
 
0.1%
41636
 
0.1%
-1833
 
0.1%
240032
 
0.1%
Other values (20594)25330
84.4%
ValueCountFrequency (%)
-3396031
< 0.1%
-2090511
< 0.1%
-1509531
< 0.1%
-946251
< 0.1%
-738951
< 0.1%
-570601
< 0.1%
-514431
< 0.1%
-511831
< 0.1%
-466271
< 0.1%
-457341
< 0.1%
ValueCountFrequency (%)
9616641
< 0.1%
6999441
< 0.1%
5686381
< 0.1%
5277111
< 0.1%
5275661
< 0.1%
5149751
< 0.1%
5137981
< 0.1%
5119051
< 0.1%
5013701
< 0.1%
4991001
< 0.1%

previous_payment_sept
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:38.216967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
MonotonicityNot monotonic
2023-11-06T11:34:38.478861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05249
 
17.5%
20001363
 
4.5%
3000891
 
3.0%
5000698
 
2.3%
1500507
 
1.7%
4000426
 
1.4%
10000401
 
1.3%
1000365
 
1.2%
2500298
 
1.0%
6000294
 
1.0%
Other values (7933)19508
65.0%
ValueCountFrequency (%)
05249
17.5%
19
 
< 0.1%
214
 
< 0.1%
315
 
0.1%
418
 
0.1%
512
 
< 0.1%
615
 
0.1%
79
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
8735521
< 0.1%
5050001
< 0.1%
4933581
< 0.1%
4239031
< 0.1%
4050161
< 0.1%
3681991
< 0.1%
3230141
< 0.1%
3048151
< 0.1%
3020001
< 0.1%
3000391
< 0.1%

previous_payment_aug
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:38.760801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
MonotonicityNot monotonic
2023-11-06T11:34:39.052262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05396
 
18.0%
20001290
 
4.3%
3000857
 
2.9%
5000717
 
2.4%
1000594
 
2.0%
1500521
 
1.7%
4000410
 
1.4%
10000318
 
1.1%
6000283
 
0.9%
2500251
 
0.8%
Other values (7889)19363
64.5%
ValueCountFrequency (%)
05396
18.0%
115
 
0.1%
220
 
0.1%
318
 
0.1%
411
 
< 0.1%
525
 
0.1%
68
 
< 0.1%
712
 
< 0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
16842591
< 0.1%
12270821
< 0.1%
12154711
< 0.1%
10245161
< 0.1%
5804641
< 0.1%
4155521
< 0.1%
4010031
< 0.1%
3881261
< 0.1%
3852281
< 0.1%
3849861
< 0.1%

previous_payment_july
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:39.357036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
MonotonicityNot monotonic
2023-11-06T11:34:39.619444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05968
 
19.9%
20001285
 
4.3%
10001103
 
3.7%
3000870
 
2.9%
5000721
 
2.4%
1500490
 
1.6%
4000381
 
1.3%
10000312
 
1.0%
1200243
 
0.8%
6000241
 
0.8%
Other values (7508)18386
61.3%
ValueCountFrequency (%)
05968
19.9%
113
 
< 0.1%
219
 
0.1%
314
 
< 0.1%
415
 
0.1%
518
 
0.1%
614
 
< 0.1%
718
 
0.1%
810
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8960401
< 0.1%
8890431
< 0.1%
5082291
< 0.1%
4175881
< 0.1%
4009721
< 0.1%
3970921
< 0.1%
3804781
< 0.1%
3717181
< 0.1%
3493951
< 0.1%
3442611
< 0.1%

previous_payment_june
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076867
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:39.891018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
MonotonicityNot monotonic
2023-11-06T11:34:40.156976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06408
 
21.4%
10001394
 
4.6%
20001214
 
4.0%
3000887
 
3.0%
5000810
 
2.7%
1500441
 
1.5%
4000402
 
1.3%
10000341
 
1.1%
2500259
 
0.9%
500258
 
0.9%
Other values (6927)17586
58.6%
ValueCountFrequency (%)
06408
21.4%
122
 
0.1%
222
 
0.1%
313
 
< 0.1%
420
 
0.1%
512
 
< 0.1%
616
 
0.1%
711
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6210001
< 0.1%
5288971
< 0.1%
4970001
< 0.1%
4321301
< 0.1%
4000461
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3200081
< 0.1%
3130941
< 0.1%
2929621
< 0.1%

previous_payment_may
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:40.437466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
MonotonicityNot monotonic
2023-11-06T11:34:40.688597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06703
 
22.3%
10001340
 
4.5%
20001323
 
4.4%
3000947
 
3.2%
5000814
 
2.7%
1500426
 
1.4%
4000401
 
1.3%
10000343
 
1.1%
500250
 
0.8%
6000247
 
0.8%
Other values (6887)17206
57.4%
ValueCountFrequency (%)
06703
22.3%
121
 
0.1%
213
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
59
 
< 0.1%
67
 
< 0.1%
79
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
4265291
< 0.1%
4179901
< 0.1%
3880711
< 0.1%
3792671
< 0.1%
3320001
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3268891
< 0.1%
3170771
< 0.1%
3101351
< 0.1%

previous_payment_april
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502567
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-11-06T11:34:40.962903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
MonotonicityNot monotonic
2023-11-06T11:34:41.246265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07173
23.9%
10001299
 
4.3%
20001295
 
4.3%
3000914
 
3.0%
5000808
 
2.7%
1500439
 
1.5%
4000411
 
1.4%
10000356
 
1.2%
500247
 
0.8%
6000220
 
0.7%
Other values (6929)16838
56.1%
ValueCountFrequency (%)
07173
23.9%
120
 
0.1%
29
 
< 0.1%
314
 
< 0.1%
412
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
5286661
< 0.1%
5271431
< 0.1%
4430011
< 0.1%
4220001
< 0.1%
4035001
< 0.1%
3770001
< 0.1%
3724951
< 0.1%
3512821
< 0.1%
3452931
< 0.1%
3080001
< 0.1%

target
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Length

2023-11-06T11:34:41.487656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-11-06T11:34:41.684378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Interactions

2023-11-06T11:34:21.596524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:40.428193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:46.708319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:52.051844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:56.854109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:01.847597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:06.962105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:11.440093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:16.417255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:21.453329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:26.663584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:31.302649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:36.798975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:41.874608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:46.401220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:51.275449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:56.764624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:01.945516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:07.151141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:12.122540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:16.860807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:21.885054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:40.715201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:46.971970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:52.330113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:57.078012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:02.096670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:07.164749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:11.662118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:16.640020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:21.731696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:26.903070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:31.510096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:37.071388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:42.094044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:46.598211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:51.556110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:57.036181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:02.169517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:07.412093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:12.347569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:17.081196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:22.162789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:40.994877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:47.226967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:52.620061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:57.287218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:02.338919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:07.378983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:11.871861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:16.885060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:22.002202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:27.113014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:31.723997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:37.332222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:42.310436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:46.804037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:51.830789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:57.307144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:02.400880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:07.677625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:12.563541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:17.299278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:22.443952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:41.279177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:47.494467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:52.885176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:57.500917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:02.590686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:07.601001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:12.086274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:17.130936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:22.266428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:27.332709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:31.934036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:37.587417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:42.531206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:47.025217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:52.105004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:57.564738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:02.632492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:07.941933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:12.784138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:17.513053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:22.730450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:41.547179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:47.754479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:53.096035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:57.719395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:02.822613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:07.802069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:12.281026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:17.372062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:22.531333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:27.537870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:32.155296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:37.839027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:42.742689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:47.227963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-11-06T11:33:00.210993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:05.236063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:09.944020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:14.880737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:19.776517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:24.914302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:29.740064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:34.575007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:40.291102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:44.908387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:49.533040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:55.075058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:59.910061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:05.334636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:10.591985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:15.294462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:19.823982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:25.626581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:45.165058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:50.561096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:55.545322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:00.477978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:05.504190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:10.163510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:15.091414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:20.038287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:25.168989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:29.972108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:34.843864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:40.525446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:45.132069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:49.789339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:55.319020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:00.135789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:05.577533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:10.833129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:15.527937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:20.096898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:25.873943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:45.395761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:50.812079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:55.750850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:00.716712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:05.741400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:10.373575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:15.303137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:20.259380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:25.406595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:30.195858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:35.567008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:40.748047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:45.335433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:50.036014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:55.533870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:00.338593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:05.828972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:11.051663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:15.736526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:20.333463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:26.151946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:45.669164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:51.076094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:55.985042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:00.948135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:06.033343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:10.604496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:15.530711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:20.483135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:25.675661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:30.440554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:35.822595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:40.997518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:45.565801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:50.301537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:55.774520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:01.148986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:06.103837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:11.287011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:15.975147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:20.607197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:26.396781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:45.922034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:51.289104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:56.193490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:01.148160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:06.275115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:10.814519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:15.737136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:20.705979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:25.914539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:30.650355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:36.062098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:41.211668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:45.758126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:50.536643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:56.000613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:01.354657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:06.365875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:11.503262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:16.185995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:20.841992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:26.653316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:46.186237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:51.545067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:56.423472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:01.368109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:06.521092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:11.033038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:15.950401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:20.964077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:26.176067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:30.861941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:36.319616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:41.432522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:45.987083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:50.787302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:56.241096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:01.547528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:06.620944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:11.720813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:16.413408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:21.094903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:26.877538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:46.428573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:51.783124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:32:56.630770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:01.593374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:06.736703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:11.233002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:16.177258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:21.203020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:26.411530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:31.079960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:36.550533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:41.651033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:46.196070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:51.025088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:33:56.497410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:01.737866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:06.869776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:11.901877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:16.628030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-06T11:34:21.324091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-11-06T11:34:41.915511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-11-06T11:34:42.532529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-11-06T11:34:43.078090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-11-06T11:34:43.553536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-11-06T11:34:43.799375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-11-06T11:34:27.277994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-06T11:34:28.847005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEstatus_payment_septstatus_payment_augstatus_payment_julystatus_payment_junestatus_payment_maystatus_payment_aprilbill_septbill_augbill_julybill_junebill_maybill_aprilprevious_payment_septprevious_payment_augprevious_payment_julyprevious_payment_juneprevious_payment_mayprevious_payment_apriltarget
0120000.02212422-1-1-2-23913.03102.0689.00.00.00.00.0689.00.00.00.00.01
12120000.022226-1200022682.01725.02682.03272.03455.03261.00.01000.01000.01000.00.02000.01
2390000.02223400000029239.014027.013559.014331.014948.015549.01518.01500.01000.01000.01000.05000.00
3450000.02213700000046990.048233.049291.028314.028959.029547.02000.02019.01200.01100.01069.01000.00
4550000.012157-10-10008617.05670.035835.020940.019146.019131.02000.036681.010000.09000.0689.0679.00
5650000.01123700000064400.057069.057608.019394.019619.020024.02500.01815.0657.01000.01000.0800.00
67500000.011229000000367965.0412023.0445007.0542653.0483003.0473944.055000.040000.038000.020239.013750.013770.00
78100000.0222230-1-100-111876.0380.0601.0221.0-159.0567.0380.0601.00.0581.01687.01542.00
89140000.02312800200011285.014096.012108.012211.011793.03719.03329.00.0432.01000.01000.01000.00
91020000.013235-2-2-2-2-1-10.00.00.00.013007.013912.00.00.00.013007.01122.00.00

Last rows

IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEstatus_payment_septstatus_payment_augstatus_payment_julystatus_payment_junestatus_payment_maystatus_payment_aprilbill_septbill_augbill_julybill_junebill_maybill_aprilprevious_payment_septprevious_payment_augprevious_payment_julyprevious_payment_juneprevious_payment_mayprevious_payment_apriltarget
2999029991140000.012141000000138325.0137142.0139110.0138262.049675.046121.06000.07000.04228.01505.02000.02000.00
2999129992210000.0121343222222500.02500.02500.02500.02500.02500.00.00.00.00.00.00.01
299922999310000.013143000-2-2-28802.010400.00.00.00.00.02000.00.00.00.00.00.00
2999329994100000.0112380-1-10003042.01427.0102996.070626.069473.055004.02000.0111784.04000.03000.02000.02000.00
299942999580000.01223422222272557.077708.079384.077519.082607.081158.07000.03500.00.07000.00.04000.01
2999529996220000.013139000000188948.0192815.0208365.088004.031237.015980.08500.020000.05003.03047.05000.01000.00
2999629997150000.013243-1-1-1-1001683.01828.03502.08979.05190.00.01837.03526.08998.0129.00.00.00
299972999830000.012237432-1003565.03356.02758.020878.020582.019357.00.00.022000.04200.02000.03100.01
299982999980000.0131411-1000-1-1645.078379.076304.052774.011855.048944.085900.03409.01178.01926.052964.01804.01
299993000050000.01214600000047929.048905.049764.036535.032428.015313.02078.01800.01430.01000.01000.01000.01